Profiling is a process that relies on matching your customer or prospect record to public information from data compilers and enhancing your record with the selected information (for example, NAICS code and com- pany size). That may sound easy, but it’s not! The problems lie primarily in matching your customer record with the same company record in the public database. In many cases, the way in which your customer or pros- pect record was input may not match the way in which the same company record is input in the outside database. Since I came from the chemical industry, let’s use DuPont as an example. There are multiple ways that DuPont could be listed. Here are a few:
• DuPont
• DuPont & Company
• DuPont de Nemours
• The DuPont Company
• duPont (fill in the name of the division and all the variations) If your spelling differs from the spelling used in the outside database, the matching process may not find the record, as it doesn’t see the basis for the match. Several years ago, I got a call from Cheryl Perkins, who had attended one of my seminars. I use the DuPont example in these sessions, and her company, Chesterton, sells DuPont. So, when she went back to work, she looked DuPont up on Chesterton’s large customer database. It had seventy-three different listings for DuPont—an obvious problem if Chesterton wanted to see what its total sales were to this customer.
Among the seventy-three listings were several duplicates of the same plant, in addition to multiple divisions of DuPont.
This does not even take into account the mailing address for a com- pany, which has its own set of problems in addition to the “official”
address of the location. Larger companies can have as many as three legal addresses. There is the “front door” address (usually the one that the pub- lic databases carry); the billing address, which may be a P.O. box; and even the shipping address, which could be a receiving dock. The last two addresses frequently appear on the customer records, particularly if they come from accounting. As one can see, the possible variations are multi- ple and will potentially cause the computer matching software program to conclude that the two records are different even though they are actu- ally the same company and location.
Several years ago I worked closely with Frank Wagner, who was pres- ident of ESA Direct, a Cleveland-based B2B computer service bureau that has since been absorbed by Lee Marketing Services in Dallas. They saw lots of customer files over the years and developed a list of the six most common data errors that caused non-matches when attempting to com- bine both company and contact records. As a guide for your quality con- trol on data input procedures, here’s what Frank found. They are listed in descending order of frequency.
1. Different address/same company 2. Transposed characters at data entry 3. Different spelling/same person 4. Last name only/no first name 5. Different company spelling 6. No company name
Because of this problem, match rates between customer records and outside databases are usually only in the 60 percent to 70 percent range.
That is, even though the information on the company is in the larger pub- lic database, the software can’t make the match. This difficulty is known, and many computer service bureaus have written special algorithms in an attempt to solve the problem. These programs work better than consumer matching software but still not well enough to get much above the 70 percent matching rate.
So, now that you’re aware of the problem, what do you do? Well, first of all, ensure that the addresses of the customers in your database are postal certified by running them through CASS certification. This pro- gram verifies that the address you have is the same one the post office has and therefore is used as the “official” address of the site. Once this is complete, run the file through the National Change of Address (NCOA) file to catch those companies who may have moved in the past 12 to 18 months. Any computer service bureau will be able to handle the CASS and NCOA processes. Then, assuming that you are using an outside computer service bureau or database provider to perform the matching and enhance- ment process, be aware of these matching issues and inquire as to their unique processes and capabilities to deal with this problem. The Resource Directory at the end of this book includes a list of firms that have expe- rience in B2B merge/purge and enhancement processes. When the matched and enhanced list is returned, the next step, as pointed out in the Fairy- tale Brownies example, may be to manually look up the nonmatched records.
There is another solution for in-house matching, and D&B Market- ing Solutions provides that. The company has a desktop software pro- gram that contains its database and a system of matching named Market Place Gold. This may be a wise investment in any case, because when new customers or prospects are sold or found, the data enhancement process should be done for each new record. It will be cheaper to do this in-house rather than send these new records to an outside firm, given that the num- ber will be small.
Now let’s see how this profiling may look once it’s completed. Table 3.1 shows an example of a customer profile matched to the SIC code.
This is just a standard table showing how many companies matched spe- cific SIC codes in numeric sequence. The second column shows the per- centage of the customer base that these matches represented.
The first-level results, as depicted in Table 3.1, clearly indicated that there were some very good clusters by industry—that is, there were more customers in certain industries than others. In this example, commercial printing, lithographic (81 customers), periodicals (46 customers), and plastic products (32 customers) led the list. If we stopped the data analy- sis here, then these three industry groups (followed by other high cus- tomer count segments) would be the ones to target for future marketing
Number of Percent of 4-Digit SIC 4-Digit SIC Code Description Customers Customers
0782 Lawn and garden services 15 0.3
1521 Single-family housing construction 14 0.3
1711 Plumbing, heating, air-conditioning 10 0.2
1731 Electrical work 15 0.3
1799 Special trade contractors, not
elsewhere classified 8 0.1
2084 Wines, brandy, and brandy spirits 9 0.2
2653 Corrugated and solid fiber boxes 16 0.3
2677 Envelopes 14 0.3
2711 Newspapers 12 0.2
2721 Periodicals 46 0.9
2731 Book publishing 14 0.3
2741 Miscellaneous publishing 21 0.4
2752 Commercial printing, lithographic 81 1.5
2759 Commercial printing, not
elsewhere classified 24 0.4
2834 Pharmaceutical preparations 13 0.2
3089 Plastics products, not elsewhere
classified 32 0.6
3444 Sheet metalwork 15 0.3
3469 Metal stampings, not elsewhere
classified 12 0.2
3471 Plating and polishing 8 0.1
3499 Fabricated metal products, not
elsewhere classified 7 0.1
Table 3.1
efforts. This “mirror image” marketing approach is based on a basic mar- keting tenet. Simply put, past success is a predictor of future success.
Therefore, one of the first uses of profiling is to find and then target mar- ket segments where past success has been achieved. This assumes that the customer base has been achieved by broad marketing and sales efforts.
At times, the customer distribution by industry is skewed based on past programs that have targeted certain markets and therefore higher cus- tomer counts appear in these industries. This will obviously affect the analysis. In my experience, though, the customer base is usually spread over a larger number of SIC or NAICS codes than the company realizes, even if they initially targeted certain industries. In the profiles that I’ve done, there has never been one where an insight hasn’t been uncovered as to the composition of the customer base. The primary reason is that in spite of the firm’s success, no one really ever performed this type of industry analysis.